Weighted percentile using numpy

I don’ know what’s Weighted percentile means, but from @Joan Smith’s answer, It seems that you just need to repeat every element in ar, you can use numpy.repeat():

import numpy as np
np.repeat([1,2,3], [4,5,6])

the result is:

array([1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3])

As mentioned in comments, simply repeating values is impossible for float weights, and impractical for very large datasets. There is a library that does weighted percentiles here:
http://kochanski.org/gpk/code/speechresearch/gmisclib/gmisclib.weighted_percentile-module.html
It worked for me.

I use this function for my needs:

def quantile_at_values(values, population, weights=None):
    values = numpy.atleast_1d(values).astype(float)
    population = numpy.atleast_1d(population).astype(float)
    # if no weights are given, use equal weights
    if weights is None:
        weights = numpy.ones(population.shape).astype(float)
        normal = float(len(weights))
    # else, check weights                  
    else:                                           
        weights = numpy.atleast_1d(weights).astype(float)
        assert len(weights) == len(population)
        assert (weights >= 0).all()
        normal = numpy.sum(weights)                    
        assert normal > 0.
    quantiles = numpy.array([numpy.sum(weights[population <= value]) for value in values]) / normal
    assert (quantiles >= 0).all() and (quantiles <= 1).all()
    return quantiles

• It is vectorized as far as I could go.
• It has a lot of sanity checks.
• It works with floats as weights.
• It can work without weights (→ equal weights).
• It can compute multiple quantiles at once.

Multiply results by 100 if you want percentiles instead of quantiles.

Completely vectorized numpy solution

Here is the code I use. It’s not an optimal one (which I’m unable to write with numpy), but still much faster and more reliable than accepted solution

def weighted_quantile(values, quantiles, sample_weight=None, 
                      values_sorted=False, old_style=False):
    """ Very close to numpy.percentile, but supports weights.
    NOTE: quantiles should be in [0, 1]!
    :param values: numpy.array with data
    :param quantiles: array-like with many quantiles needed
    :param sample_weight: array-like of the same length as `array`
    :param values_sorted: bool, if True, then will avoid sorting of
        initial array
    :param old_style: if True, will correct output to be consistent
        with numpy.percentile.
    :return: numpy.array with computed quantiles.
    """
    values = np.array(values)
    quantiles = np.array(quantiles)
    if sample_weight is None:
        sample_weight = np.ones(len(values))
    sample_weight = np.array(sample_weight)
    assert np.all(quantiles >= 0) and np.all(quantiles <= 1), 
        'quantiles should be in [0, 1]'

    if not values_sorted:
        sorter = np.argsort(values)
        values = values[sorter]
        sample_weight = sample_weight[sorter]

    weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight
    if old_style:
        # To be convenient with numpy.percentile
        weighted_quantiles -= weighted_quantiles[0]
        weighted_quantiles /= weighted_quantiles[-1]
    else:
        weighted_quantiles /= np.sum(sample_weight)
    return np.interp(quantiles, weighted_quantiles, values)

Examples:

weighted_quantile([1, 2, 9, 3.2, 4], [0.0, 0.5, 1.])

array([ 1. , 3.2, 9. ])

weighted_quantile([1, 2, 9, 3.2, 4], [0.0, 0.5, 1.], sample_weight=[2, 1, 2, 4, 1])

array([ 1. , 3.2, 9. ])

A quick solution, by first sorting and then interpolating:

def weighted_percentile(data, percents, weights=None):
    ''' percents in units of 1%
        weights specifies the frequency (count) of data.
    '''
    if weights is None:
        return np.percentile(data, percents)
    ind=np.argsort(data)
    d=data[ind]
    w=weights[ind]
    p=1.*w.cumsum()/w.sum()*100
    y=np.interp(percents, p, d)
    return y

def weighted_percentile(a, percentile = np.array([75, 25]), weights=None):
    """
    O(nlgn) implementation for weighted_percentile.
    """
    percentile = np.array(percentile)/100.0
    if weights is None:
        weights = np.ones(len(a))
    a_indsort = np.argsort(a)
    a_sort = a[a_indsort]
    weights_sort = weights[a_indsort]
    ecdf = np.cumsum(weights_sort)

    percentile_index_positions = percentile * (weights.sum()-1)+1
    # need the 1 offset at the end due to ecdf not starting at 0
    locations = np.searchsorted(ecdf, percentile_index_positions)

    out_percentiles = np.zeros(len(percentile_index_positions))

    for i, empiricalLocation in enumerate(locations):
        # iterate across the requested percentiles 
        if ecdf[empiricalLocation-1] == np.floor(percentile_index_positions[i]):
            # i.e. is the percentile in between 2 separate values
            uppWeight = percentile_index_positions[i] - ecdf[empiricalLocation-1]
            lowWeight = 1 - uppWeight

            out_percentiles[i] = a_sort[empiricalLocation-1] * lowWeight + 
                                 a_sort[empiricalLocation] * uppWeight
        else:
            # i.e. the percentile is entirely in one bin
            out_percentiles[i] = a_sort[empiricalLocation]

    return out_percentiles

This is my function, it give identical behaviour to

np.percentile(np.repeat(a, weights), percentile)

With less memory overhead. np.percentile is an O(n) implementation so it’s potentially faster for small weights.
It has all the edge cases sorted out – it’s an exact solution. The interpolation answers above assume linear, when it’s a step for most of the case, except when the weight is 1.

Say we have data [1,2,3] with weights [3, 11, 7] and I want the 25% percentile. My ecdf is going to be [3, 10, 21] and I’m looking for the 5th value. The interpolation will see [3,1] and [10, 2] as the matches and interpolate giving 1.28 despite being entirely in the 2nd bin with a value of 2.

Apologies for the additional (unoriginal) answer (not enough rep to comment on @nayyarv’s). His solution worked for me (ie. it replicates the default behavior of np.percentage), but I think you can eliminate the for loop with clues from how the original np.percentage is written.

def weighted_percentile(a, q=np.array([75, 25]), w=None):
    """
    Calculates percentiles associated with a (possibly weighted) array

    Parameters
    ----------
    a : array-like
        The input array from which to calculate percents
    q : array-like
        The percentiles to calculate (0.0 - 100.0)
    w : array-like, optional
        The weights to assign to values of a.  Equal weighting if None
        is specified

    Returns
    -------
    values : np.array
        The values associated with the specified percentiles.  
    """
    # Standardize and sort based on values in a
    q = np.array(q) / 100.0
    if w is None:
        w = np.ones(a.size)
    idx = np.argsort(a)
    a_sort = a[idx]
    w_sort = w[idx]

    # Get the cumulative sum of weights
    ecdf = np.cumsum(w_sort)

    # Find the percentile index positions associated with the percentiles
    p = q * (w.sum() - 1)

    # Find the bounding indices (both low and high)
    idx_low = np.searchsorted(ecdf, p, side='right')
    idx_high = np.searchsorted(ecdf, p + 1, side='right')
    idx_high[idx_high > ecdf.size - 1] = ecdf.size - 1

    # Calculate the weights 
    weights_high = p - np.floor(p)
    weights_low = 1.0 - weights_high

    # Extract the low/high indexes and multiply by the corresponding weights
    x1 = np.take(a_sort, idx_low) * weights_low
    x2 = np.take(a_sort, idx_high) * weights_high

    # Return the average
    return np.add(x1, x2)

# Sample data
a = np.array([1.0, 2.0, 9.0, 3.2, 4.0], dtype=np.float)
w = np.array([2.0, 1.0, 3.0, 4.0, 1.0], dtype=np.float)

# Make an unweighted "copy" of a for testing
a2 = np.repeat(a, w.astype(np.int))

# Tests with different percentiles chosen
q1 = np.linspace(0.0, 100.0, 11)
q2 = np.linspace(5.0, 95.0, 10)
q3 = np.linspace(4.0, 94.0, 10)
for q in (q1, q2, q3):
    assert np.all(weighted_percentile(a, q, w) == np.percentile(a2, q))

here my solution:

def my_weighted_perc(data,perc,weights=None):
    if weights==None:
        return nanpercentile(data,perc)
    else:
        d=data[(~np.isnan(data))&(~np.isnan(weights))]
        ix=np.argsort(d)
        d=d[ix]
        wei=weights[ix]
        wei_cum=100.*cumsum(wei*1./sum(wei))
        return interp(perc,wei_cum,d)

it simply calculates the weighted CDF of the data and then it uses to estimate the weighted percentiles.

The weightedcalcs package supports quantiles:

import weightedcalcs as wc
import pandas as pd

df = pd.DataFrame({'v': [1, 2, 3], 'w': [3, 2, 1]})
calc = wc.Calculator('w')  # w designates weight

calc.quantile(df, 'v', 0.5)
# 1.5

Cleaner and simpler using this reference for weighted percentile method.

import numpy as np

def weighted_percentile(data, weights, perc):
    """
    perc : percentile in [0-1]!
    """
    ix = np.argsort(data)
    data = data[ix] # sort data
    weights = weights[ix] # sort weights
    cdf = (np.cumsum(weights) - 0.5 * weights) / np.sum(weights) # 'like' a CDF function
    return np.interp(perc, cdf, data)

This seems to be now implemented in statsmodels

from statsmodels.stats.weightstats import DescrStatsW
wq = DescrStatsW(data=np.array([1, 2, 9, 3.2, 4]), weights=np.array([0.0, 0.5, 1.0, 0.3, 0.5]))
wq.quantile(probs=np.array([0.1, 0.9]), return_pandas=False)
# array([2., 9.])

The DescrStatsW object also has other methods implemented, such as weighted mean, etc. https://www.statsmodels.org/stable/generated/statsmodels.stats.weightstats.DescrStatsW.html